Ultrasound imaging provides a real-time image with information about the interior of an object or a subject such as tissue, organs, etc. In addition, ultrasound imaging can visualize the flow inside of a cavity in real-time such as the flow of blood cells inside of blood vessels. Conventional Color Flow Mapping (CFM) is one approach to estimate and visualize the flow inside blood vessels. In one instance, the CFM image is super-imposed on a black-and-white (B-mode) image that shows the tissue structures. The CFM image can be realized efficiently and made feasible using a low-cost velocity estimator.
CFM imaging has typically been limited to a smaller region of interest (ROI) of the whole image view relative to the B-mode image in order to achieve high enough frame-rates. This frame-rate issue has been addressed in current ultrasound systems utilizing parallel data acquisition. However, even with full depth color flow view and good frame-rates, is it an obstacle to actually visualize unbroken blood-flow information in the full penetration depth with a conventional color flow velocity estimator, e.g., at least due to very large variation in the dynamic range of the input signal.
The gain for Doppler-based systems may not be controlled as a time gain compensation (TGC) as for B-mode, which in general is used to compensate for the depth dependent tissue attenuation, as time-dependent gain changes will introduce false velocity artifacts into the CFM image. Hence, when the user-adjustable gain setting is high (e.g. 60 dB), the flow from the weakest signal at the bottom of the image becomes visible, but the flow from the strong signal at the skin-surface close to the transducer breaks up or disappears. When the gain setting is low (e.g. 40 dB), the flow at the top of the image reappears but the flow at the bottom disappears.
This is illustrated in FIGS. 1 and 2, which show an ultrasound transducer array 102 next to the skin 104 and vascular structure 106 in the field of view 108. The vascular structure 106 includes first vascular structure 110 in the near field 112 (from just below the surface to e.g. 8 cm) and second vascular structure 114 in the far field 116 (to e.g. 18 cm). FIG. 1 shows an example of lower gain, where only the first vascular structure 110 in the near field 112 (the shaded structure) is properly visualized. FIG. 2 shows an example of higher gain, where only the second vascular structure 114 in the far field 116 (the shaded structure) is properly visualized.
A reason for the above is the dynamic range of the CFM processing, which is limited to operating on complex samples of 2×16-bit fixed-point values (integer values). This is required partly to restrict the requirements for access to fast temporal storage (central processing unit (CPU), digital signal processor (DSP) or graphics processing unit (GPU) cache memory) resources. Calculating a full-view frame of CFM for display may require 10 MB (Megabytes) of temporal data, which easily becomes an issue with respect to the available resources. Unfortunately, doubling this by simply going from 2×16-bit fixed-point to 2×32-bit floating-point will have a significant impact on performance (frame rate). A vector flow imaging (VFI) system may require up to three (3) times the resources, and, thus, will have an even greater significant impact on the performance.